Because of its simplicity and efficieny in navigating large search spaces for optimal solutions, particle swarm optimizers (PSOs) are used in this research to develop efficient, robust and flexible unsupervised image clustering algorithms. Both hard (crisp) and fuzzy clustering are being studied and comparison with the well known image clustering techniques is being conducted. Furthermore, a PSO algorithm which dynamically determine the number of clusters in the image set (i.e., fully unsupervised image clustering) will be developed. The influence of the number of particles, number of iterations, and other PSO parameters on the performance of the PSO will be explored.